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- Abecassis-Moedas, Celine, et al.
(author)
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Key resources and internationalization modes of creative knowledge-intensive business services : The case of design consultancies
- 2012
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In: Creativity and Innovation Management. - : Wiley. - 0963-1690 .- 1467-8691. ; 21:3, s. 315-331
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Journal article (peer-reviewed)abstract
- In the knowledge economy, knowledge-intensive business services (KIBS) are a key driver for innovation and competitiveness. The internationalization of these businesses raises challenges given their specificities such as knowledge intensity, the importance of customer interaction and intimacy in service delivery. This paper focuses on design consultancies as a specific type of creative KIBS for which these characteristics are emphasized. The objective of this research is to analyse the resources leveraged by the firms to compete internationally. It is based on 11 case studies of design consultancies located in five different countries (France, Italy, Portugal, Spain and the UK) that were selected for their capacity to perform at the international level for several years. The paper advances three internationalization modes depending on contingent variables and focusing on specific resources that enable international competitiveness: star-based, process-based and glocality-based. In star-based creative KIBS, the individual designer has developed a reputation that attracts customers internationally, operating as a brand. In process-based creative KIBS, the reputation of a collective creative process attracts clients from other countries. In glocality-based creative KIBS, the geographical proximity obtained by opening international offices helps to develop a close understanding of the client through frequent interactions, and also to know the client's market well and to better understand local codes and signs. These modes complement those presented in the existing internationalization literature which takes the peculiarities of creative KIBS into consideration.
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| 4. |
- Abrahamsson, Henrik
(author)
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Network overload avoidance by traffic engineering and content caching
- 2012
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Doctoral thesis (other academic/artistic)abstract
- The Internet traffic volume continues to grow at a great rate, now driven by video and TV distribution. For network operators it is important to avoid congestion in the network, and to meet service level agreements with their customers. This thesis presents work on two methods operators can use to reduce links loads in their networks: traffic engineering and content caching.This thesis studies access patterns for TV and video and the potential for caching. The investigation is done both using simulation and by analysis of logs from a large TV-on-Demand system over four months.The results show that there is a small set of programs that account for a large fraction of the requests and that a comparatively small local cache can be used to significantly reduce the peak link loads during prime time. The investigation also demonstrates how the popularity of programs changes over time and shows that the access pattern in a TV-on-Demand system very much depends on the content type.For traffic engineering the objective is to avoid congestion in the network and to make better use of available resources by adapting the routing to the current traffic situation. The main challenge for traffic engineering in IP networks is to cope with the dynamics of Internet traffic demands.This thesis proposes L-balanced routings that route the traffic on the shortest paths possible but make sure that no link is utilised to more than a given level L. L-balanced routing gives efficient routing of traffic and controlled spare capacity to handle unpredictable changes in traffic. We present an L-balanced routing algorithm and a heuristic search method for finding L-balanced weight settings for the legacy routing protocols OSPF and IS-IS. We show that the search and the resulting weight settings work well in real network scenarios.
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| 5. |
- Abrahamsson, Henrik, et al.
(author)
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Program Popularity and Viewer Behaviour in a Large TV-on-Demand System
- 2012. - 7
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In: IMC '12 Proceedings of the 2012 ACM conference on Internet measurement conference. - New York, NY, USA : ACM. - 9781450317054 ; , s. 199-210
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Conference paper (peer-reviewed)abstract
- Today increasingly large volumes of TV and video are distributed over IP-networks and over the Internet. It is therefore essential for traffic and cache management to understand TV program popularity and access patterns in real networks. In this paper we study access patterns in a large TV-on-Demand system over four months. We study user behaviour and program popularity and its impact on caching. The demand varies a lot in daily and weekly cycles. There are large peaks in demand, especially on Friday and Saturday evenings, that need to be handled. We see that the cacheability, the share of requests that are not first-time requests, is very high. Furthermore, there is a small set of programs that account for a large fraction of the requests. We also find that the share of requests for the top most popular programs grows during prime time, and the change rate among them decreases. This is important for caching. The cache hit ratio increases during prime time when the demand is the highest, and aching makes the biggest difference when it matters most. We also study the popularity (in terms of number of requests and rank) of individual programs and how that changes over time. Also, we see that the type of programs offered determines what the access pattern will look like.
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- Afshar, Sara, et al.
(author)
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Resource Sharing under Multiprocessor Semi-Partitioned Scheduling
- 2012
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In: 18th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA'12). - : IEEE. - 9781467330176 - 9780769548241 ; , s. 290-299
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Conference paper (peer-reviewed)abstract
- Semi-partitioned scheduling has become the subject of recent interest for multiprocessors due to better utilization results, compared to conventional global and partitioned scheduling algorithms. Under semi-partitioned scheduling, a major group of tasks are assigned to fixed processors while a low number of tasks are allocated to more than one processor. Various task assigning techniques have recently been proposed in a semi-partitioned environment. However, a synchronization mechanism for resource sharing among tasks in semi-partitioned scheduling has not yet been investigated. In this paper we propose and evaluate two methods for handling resource sharing under semi-partitioned scheduling in multiprocessor platforms. The main challenge addressed in this paper is to serve the resource requests of tasks that are assigned to different processors.
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| 7. |
- Afshar, Sara, et al.
(author)
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Towards Resource Sharing under Multiprocessor Semi-Partitioned Scheduling
- 2012
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In: 7th IEEE International Symposium on Industrial Embedded Systems (SIES'12). - : IEEE. - 9781467326834 - 9781467326858 ; , s. 315-318
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Conference paper (peer-reviewed)abstract
- Semi-partitioned scheduling has been the subject of recent interest, compared with conventional global and partitioned scheduling algorithms for multiprocessors, due to better utilization results. In semi-partitioned scheduling most tasks are assigned to fixed processors while a low number of tasks are split up and allocated to different processors. Various techniques have recently been proposed to assign tasks in a semi-partitioned environment. However, an appropriate resource sharing mechanism for handling the resource requests between tasks in semi-partitioned scheduling has not yet been investigated. In this paper we propose two methods for handling resource sharing under semi-partitioned scheduling in multiprocessor platforms. The main challenge is to handle the resource requests of tasks that are split over multiple processors.
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| 8. |
- Afzal, Wasif, et al.
(author)
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Resampling Methods in Software Quality Classification
- 2012
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In: International Journal of Software Engineering and Knowledge Engineering. - Sweden : World Scientific. - 0218-1940. ; 22:2, s. 203-223
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Journal article (peer-reviewed)abstract
- In the presence of a number of algorithms for classification and prediction in software engineering, there is a need to have a systematic way of assessing their performances. The performance assessment is typically done by some form of partitioning or resampling of the original data to alleviate biased estimation. For predictive and classification studies in software engineering, there is a lack of a definitive advice on the most appropriate resampling method to use. This is seen as one of the contributing factors for not being able to draw general conclusions on what modeling technique or set of predictor variables are the most appropriate. Furthermore, the use of a variety of resampling methods make it impossible to perform any formal meta-analysis of the primary study results. Therefore, it is desirable to examine the influence of various resampling methods and to quantify possible differences. Objective and method: This study empirically compares five common resampling methods (hold-out validation, repeated random sub-sampling, 10-fold cross-validation, leave-one-out cross-validation and non-parametric bootstrapping) using 8 publicly available data sets with genetic programming (GP) and multiple linear regression (MLR) as software quality classification approaches. Location of (PF, PD) pairs in the ROC (receiver operating characteristics) space and area under an ROC curve (AUC) are used as accuracy indicators. Results: The results show that in terms of the location of (PF, PD) pairs in the ROC space, bootstrapping results are in the preferred region for 3 of the 8 data sets for GP and for 4 of the 8 data sets for MLR. Based on the AUC measure, there are no significant differences between the different resampling methods using GP and MLR. Conclusion: There can be certain data set properties responsible for insignificant differences between the resampling methods based on AUC. These include imbalanced data sets, insignificant predictor variables and high-dimensional data sets. With the current selection of data sets and classification techniques, bootstrapping is a preferred method based on the location of (PF, PD) pair data in the ROC space. Hold-out validation is not a good choice for comparatively smaller data sets, where leave-one-out cross-validation (LOOCV) performs better. For comparatively larger data sets, 10-fold cross-validation performs better than LOOCV.
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| 9. |
- Agasisti, Tommaso, et al.
(author)
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Evaluating the performance of academic departments: an analysis of research-related output efficiency
- 2012
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In: Research Evaluation. - : Oxford University Press (OUP). - 0958-2029 .- 1471-5449. ; 21:1, s. 2-14
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Journal article (peer-reviewed)abstract
- In this article we investigated whether academic departments do experience a trade-off among different research outputs. More specifically, we define four types of academic research outputs: quantity (publications); quality (citation indexes); research funds obtained through research grants; and applied research funds obtained through external orders. Subsequently, we define a department's performance through the concept of efficiency, namely the ability to maximize academic research output given an amount of inputs (facilities and human resources). Using Data Envelopment Analysis (DEA), we measure efficiency for 69 academic departments (focused on scientific subjects) located in the Lombardy Region (Italy), benefiting from a unique data set containing detailed information on research inputs and outputs. The empirical analysis shows that efficiency rankings change significantly when considering different research-related outputs and thus it highlights different research strategies among the academic departments. These different strategies emerge also considering jointly all four types of outputs: the academic departments focus on different outputs in order to obtain the highest overall efficiency scores. In the last section, policy and managerial implications have been discussed.
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| 10. |
- Ahmed, Mobyen Uddin, et al.
(author)
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A Computer Aided System for Post-operative Pain Treatment Combining Knowledge Discovery and Case-Based Reasoning
- 2012
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In: Lecture Notes in Computer Science, vol. 7466. - Berlin, Heidelberg : Springer. - 9783642329852 ; , s. 3-16
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Book chapter (peer-reviewed)abstract
- The quality improvement for individual postoperative-pain treatment is an important issue. This paper presents a computer aided system for physicians in their decision making tasks in post-operative pain treatment. Here, the system combines a Case-Based Reasoning (CBR) approach with knowledge discovery. Knowledge discovery is applied in terms of clustering in order to identify the unusual cases. We applied a two layered case structure for case solutions i.e. the treatment is in the first layer and outcome after treatment (i.e. recovery of the patient) is in the second layer. Moreover, a 2nd order retrieval approach is applied in the CBR retrieval step in order to retrieve the most similar cases. The system enables physicians to make more informed decisions since they are able to explore similar both regular and rare cases of post-operative patients. The two layered case structure is moving the focus from diagnosis to outcome i.e. the recovery of the patient, something a physician is especially interested in, including the risk of complications and side effects.
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